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  <h1>Source code for torch.utils.data.dataset</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">bisect</span>
<span class="kn">import</span> <span class="nn">warnings</span>

<span class="kn">from</span> <span class="nn">torch._utils</span> <span class="kn">import</span> <span class="n">_accumulate</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">randperm</span>


<div class="viewcode-block" id="Dataset"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.Dataset">[docs]</a><span class="k">class</span> <span class="nc">Dataset</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;An abstract class representing a :class:`Dataset`.</span>

<span class="sd">    All datasets that represent a map from keys to data samples should subclass</span>
<span class="sd">    it. All subclasses should overwrite :meth:`__getitem__`, supporting fetching a</span>
<span class="sd">    data sample for a given key. Subclasses could also optionally overwrite</span>
<span class="sd">    :meth:`__len__`, which is expected to return the size of the dataset by many</span>
<span class="sd">    :class:`~torch.utils.data.Sampler` implementations and the default options</span>
<span class="sd">    of :class:`~torch.utils.data.DataLoader`.</span>

<span class="sd">    .. note::</span>
<span class="sd">      :class:`~torch.utils.data.DataLoader` by default constructs a index</span>
<span class="sd">      sampler that yields integral indices.  To make it work with a map-style</span>
<span class="sd">      dataset with non-integral indices/keys, a custom sampler must be provided.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">ConcatDataset</span><span class="p">([</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">])</span></div>

    <span class="c1"># No `def __len__(self)` default?</span>
    <span class="c1"># See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]</span>
    <span class="c1"># in pytorch/torch/utils/data/sampler.py</span>


<div class="viewcode-block" id="IterableDataset"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.IterableDataset">[docs]</a><span class="k">class</span> <span class="nc">IterableDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;An iterable Dataset.</span>

<span class="sd">    All datasets that represent an iterable of data samples should subclass it.</span>
<span class="sd">    Such form of datasets is particularly useful when data come from a stream.</span>

<span class="sd">    All subclasses should overwrite :meth:`__iter__`, which would return an</span>
<span class="sd">    iterator of samples in this dataset.</span>

<span class="sd">    When a subclass is used with :class:`~torch.utils.data.DataLoader`, each</span>
<span class="sd">    item in the dataset will be yielded from the :class:`~torch.utils.data.DataLoader`</span>
<span class="sd">    iterator. When :attr:`num_workers &gt; 0`, each worker process will have a</span>
<span class="sd">    different copy of the dataset object, so it is often desired to configure</span>
<span class="sd">    each copy independently to avoid having duplicate data returned from the</span>
<span class="sd">    workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker</span>
<span class="sd">    process, returns information about the worker. It can be used in either the</span>
<span class="sd">    dataset&#39;s :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` &#39;s</span>
<span class="sd">    :attr:`worker_init_fn` option to modify each copy&#39;s behavior.</span>

<span class="sd">    Example 1: splitting workload across all workers in :meth:`__iter__`::</span>

<span class="sd">        &gt;&gt;&gt; class MyIterableDataset(torch.utils.data.IterableDataset):</span>
<span class="sd">        ...     def __init__(self, start, end):</span>
<span class="sd">        ...         super(MyIterableDataset).__init__()</span>
<span class="sd">        ...         assert end &gt; start, &quot;this example code only works with end &gt;= start&quot;</span>
<span class="sd">        ...         self.start = start</span>
<span class="sd">        ...         self.end = end</span>
<span class="sd">        ...</span>
<span class="sd">        ...     def __iter__(self):</span>
<span class="sd">        ...         worker_info = torch.utils.data.get_worker_info()</span>
<span class="sd">        ...         if worker_info is None:  # single-process data loading, return the full iterator</span>
<span class="sd">        ...             iter_start = self.start</span>
<span class="sd">        ...             iter_end = self.end</span>
<span class="sd">        ...         else:  # in a worker process</span>
<span class="sd">        ...             # split workload</span>
<span class="sd">        ...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))</span>
<span class="sd">        ...             worker_id = worker_info.id</span>
<span class="sd">        ...             iter_start = self.start + worker_id * per_worker</span>
<span class="sd">        ...             iter_end = min(iter_start + per_worker, self.end)</span>
<span class="sd">        ...         return iter(range(iter_start, iter_end))</span>
<span class="sd">        ...</span>
<span class="sd">        &gt;&gt;&gt; # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].</span>
<span class="sd">        &gt;&gt;&gt; ds = MyIterableDataset(start=3, end=7)</span>

<span class="sd">        &gt;&gt;&gt; # Single-process loading</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=0)))</span>
<span class="sd">        [3, 4, 5, 6]</span>

<span class="sd">        &gt;&gt;&gt; # Mult-process loading with two worker processes</span>
<span class="sd">        &gt;&gt;&gt; # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=2)))</span>
<span class="sd">        [3, 5, 4, 6]</span>

<span class="sd">        &gt;&gt;&gt; # With even more workers</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=20)))</span>
<span class="sd">        [3, 4, 5, 6]</span>

<span class="sd">    Example 2: splitting workload across all workers using :attr:`worker_init_fn`::</span>

<span class="sd">        &gt;&gt;&gt; class MyIterableDataset(torch.utils.data.IterableDataset):</span>
<span class="sd">        ...     def __init__(self, start, end):</span>
<span class="sd">        ...         super(MyIterableDataset).__init__()</span>
<span class="sd">        ...         assert end &gt; start, &quot;this example code only works with end &gt;= start&quot;</span>
<span class="sd">        ...         self.start = start</span>
<span class="sd">        ...         self.end = end</span>
<span class="sd">        ...</span>
<span class="sd">        ...     def __iter__(self):</span>
<span class="sd">        ...         return iter(range(self.start, self.end))</span>
<span class="sd">        ...</span>
<span class="sd">        &gt;&gt;&gt; # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].</span>
<span class="sd">        &gt;&gt;&gt; ds = MyIterableDataset(start=3, end=7)</span>

<span class="sd">        &gt;&gt;&gt; # Single-process loading</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=0)))</span>
<span class="sd">        [3, 4, 5, 6]</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; # Directly doing multi-process loading yields duplicate data</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=2)))</span>
<span class="sd">        [3, 3, 4, 4, 5, 5, 6, 6]</span>

<span class="sd">        &gt;&gt;&gt; # Define a `worker_init_fn` that configures each dataset copy differently</span>
<span class="sd">        &gt;&gt;&gt; def worker_init_fn(worker_id):</span>
<span class="sd">        ...     worker_info = torch.utils.data.get_worker_info()</span>
<span class="sd">        ...     dataset = worker_info.dataset  # the dataset copy in this worker process</span>
<span class="sd">        ...     overall_start = dataset.start</span>
<span class="sd">        ...     overall_end = dataset.end</span>
<span class="sd">        ...     # configure the dataset to only process the split workload</span>
<span class="sd">        ...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))</span>
<span class="sd">        ...     worker_id = worker_info.id</span>
<span class="sd">        ...     dataset.start = overall_start + worker_id * per_worker</span>
<span class="sd">        ...     dataset.end = min(dataset.start + per_worker, overall_end)</span>
<span class="sd">        ...</span>

<span class="sd">        &gt;&gt;&gt; # Mult-process loading with the custom `worker_init_fn`</span>
<span class="sd">        &gt;&gt;&gt; # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))</span>
<span class="sd">        [3, 5, 4, 6]</span>

<span class="sd">        &gt;&gt;&gt; # With even more workers</span>
<span class="sd">        &gt;&gt;&gt; print(list(torch.utils.data.DataLoader(ds, num_workers=20, worker_init_fn=worker_init_fn)))</span>
<span class="sd">        [3, 4, 5, 6]</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">ChainDataset</span><span class="p">([</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">])</span></div>

    <span class="c1"># No `def __len__(self)` default?</span>
    <span class="c1"># See NOTE [ Lack of Default `__len__` in Python Abstract Base Classes ]</span>


<div class="viewcode-block" id="TensorDataset"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.TensorDataset">[docs]</a><span class="k">class</span> <span class="nc">TensorDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Dataset wrapping tensors.</span>

<span class="sd">    Each sample will be retrieved by indexing tensors along the first dimension.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        *tensors (Tensor): tensors that have the same size of the first dimension.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">tensors</span><span class="p">):</span>
        <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">tensors</span> <span class="o">=</span> <span class="n">tensors</span>

    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tensor</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">tensors</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>


<div class="viewcode-block" id="ConcatDataset"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.ConcatDataset">[docs]</a><span class="k">class</span> <span class="nc">ConcatDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Dataset as a concatenation of multiple datasets.</span>

<span class="sd">    This class is useful to assemble different existing datasets.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        datasets (sequence): List of datasets to be concatenated</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">cumsum</span><span class="p">(</span><span class="n">sequence</span><span class="p">):</span>
        <span class="n">r</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="p">[],</span> <span class="mi">0</span>
        <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">sequence</span><span class="p">:</span>
            <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
            <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">l</span> <span class="o">+</span> <span class="n">s</span><span class="p">)</span>
            <span class="n">s</span> <span class="o">+=</span> <span class="n">l</span>
        <span class="k">return</span> <span class="n">r</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">datasets</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ConcatDataset</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">datasets</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;datasets should not be an empty iterable&#39;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
            <span class="k">assert</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">IterableDataset</span><span class="p">),</span> <span class="s2">&quot;ConcatDataset does not support IterableDataset&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cumulative_sizes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>

    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cumulative_sizes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>

    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">idx</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
            <span class="k">if</span> <span class="o">-</span><span class="n">idx</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;absolute value of index should not exceed dataset length&quot;</span><span class="p">)</span>
            <span class="n">idx</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">+</span> <span class="n">idx</span>
        <span class="n">dataset_idx</span> <span class="o">=</span> <span class="n">bisect</span><span class="o">.</span><span class="n">bisect_right</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cumulative_sizes</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">dataset_idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">sample_idx</span> <span class="o">=</span> <span class="n">idx</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">sample_idx</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">cumulative_sizes</span><span class="p">[</span><span class="n">dataset_idx</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">dataset_idx</span><span class="p">][</span><span class="n">sample_idx</span><span class="p">]</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">cummulative_sizes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;cummulative_sizes attribute is renamed to &quot;</span>
                      <span class="s2">&quot;cumulative_sizes&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">cumulative_sizes</span></div>


<div class="viewcode-block" id="ChainDataset"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.ChainDataset">[docs]</a><span class="k">class</span> <span class="nc">ChainDataset</span><span class="p">(</span><span class="n">IterableDataset</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Dataset for chainning multiple :class:`IterableDataset` s.</span>

<span class="sd">    This class is useful to assemble different existing dataset streams. The</span>
<span class="sd">    chainning operation is done on-the-fly, so concatenating large-scale</span>
<span class="sd">    datasets with this class will be efficient.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        datasets (iterable of IterableDataset): datasets to be chained together</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">datasets</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ChainDataset</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span> <span class="o">=</span> <span class="n">datasets</span>

    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">IterableDataset</span><span class="p">),</span> <span class="s2">&quot;ChainDataset only supports IterableDataset&quot;</span>
            <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">d</span><span class="p">:</span>
                <span class="k">yield</span> <span class="n">x</span>

    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">total</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">IterableDataset</span><span class="p">),</span> <span class="s2">&quot;ChainDataset only supports IterableDataset&quot;</span>
            <span class="n">total</span> <span class="o">+=</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">total</span></div>


<div class="viewcode-block" id="Subset"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.Subset">[docs]</a><span class="k">class</span> <span class="nc">Subset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Subset of a dataset at specified indices.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        dataset (Dataset): The whole Dataset</span>
<span class="sd">        indices (sequence): Indices in the whole set selected for subset</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">indices</span> <span class="o">=</span> <span class="n">indices</span>

    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span>

    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span></div>


<div class="viewcode-block" id="random_split"><a class="viewcode-back" href="../../../../data.html#torch.utils.data.random_split">[docs]</a><span class="k">def</span> <span class="nf">random_split</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">lengths</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Randomly split a dataset into non-overlapping new datasets of given lengths.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        dataset (Dataset): Dataset to be split</span>
<span class="sd">        lengths (sequence): lengths of splits to be produced</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Sum of input lengths does not equal the length of the input dataset!&quot;</span><span class="p">)</span>

    <span class="n">indices</span> <span class="o">=</span> <span class="n">randperm</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">lengths</span><span class="p">))</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
    <span class="k">return</span> <span class="p">[</span><span class="n">Subset</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">indices</span><span class="p">[</span><span class="n">offset</span> <span class="o">-</span> <span class="n">length</span><span class="p">:</span><span class="n">offset</span><span class="p">])</span> <span class="k">for</span> <span class="n">offset</span><span class="p">,</span> <span class="n">length</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">_accumulate</span><span class="p">(</span><span class="n">lengths</span><span class="p">),</span> <span class="n">lengths</span><span class="p">)]</span></div>
</pre></div>

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